Numerous models were proposed for RC tasks. R-NET integrates mutual attention and self-attention into RNN encoder to refine the representation [1]}. QANET [2]} leverages similar attention in a stacked convolutional encoder to promote performance. BERT [3]} stacks multiple transformers [4]}. By applying unsupervised pre-training tasks and then fine-tuning on specific dataset, it achieves state-of-the-art performance in various NLP tasks including RC. However, none of them explores the model generalizability across different datasets, and their transferabilities still remain unknown.
